Data leakage prevention (DLP) strategy
Data leakage is when sensitive information leaves the organization without approval, often by accident. It might be a customer list forwarded to a personal email account, or a contract pasted into an AI chatbot. For a regulated organization, a leak can mean reporting duties, fines and lost customer trust. A data leakage prevention (DLP) program reduces that risk by knowing where sensitive data sits and controlling how it moves.
Begin with an access policy
The foundation is a clear company policy on who may see which data. Without one, technical tools have nothing to enforce. The policy should cover business plans, intellectual property, customer data and personally identifiable information (PII), meaning any data that can identify a person.
What the program needs
- A map of where sensitive data lives, with each type labelled by how sensitive it is.
- Access granted on a least-privilege basis, so people get only what their job requires, and reviewed regularly.
- Monitoring of the main exit routes, including email, web uploads, cloud storage and USB drives.
- Rules that block or flag policy breaches, backed by training so staff know how to handle data correctly.
Most organizations roll these out in stages, starting with the data that would cause the most harm if it leaked.
What about generative AI?
Every prompt typed into ChatGPT, Gemini or Claude is a new route for sensitive data to leave the organization. Banning AI tools outright rarely works. Staff tend to move to personal phones and laptops, where you have no visibility at all.
Nesgate covers this route inside the browser. It is a browser extension that masks sensitive data, such as passwords, ID numbers, customer records and confidential files, on the employee’s own device before the AI app receives it. Security teams can set each rule to mask, warn or block. They can also see which AI tools staff are using without approval (often called shadow AI), and every event is kept in a tamper-evident audit trail.